Self-supervised learning (SSL) has been suggested as a pre-training method to learn representations without requiring a huge amount of labeled data. Recently proposed SSL methods provide rich representations of speech which can be utilized for a broad range of spoken language tasks, and the fine-tuned models surpass supervised-only models, especially in domain with small number of labeled data [1]}, [2]}. Understanding how SSL models work is crucial to explain such success and develop a better SSL method. Previous studies have revealed acoustic, phonetic, and semantic representations encoded in representations of speech SSL, using diverse analytic tools including (non-) linear probing, mutual information, or canonical correlation analysis [3]}, [4]}, [5]}, [6]}, [7]}. Although those analyses give comprehensive insights of information process in speech SSL models, how well the representations are grounded by the actual speech production is not yet answered.
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